Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests

By Athanasios Tsanas, Max A. Little, Patrick E. McSharry, Lorraine O. Ramig. 2009

823 citations recorded by the source corpus.

Tracking Parkinson's disease (PD) symptom progression often uses the unified Parkinson's disease rating scale (UPDRS) that requires the patient's presence in clinic, and time-consuming physical examinations by trained medical staff. Thus, symptom monitoring is costly and logistically inconvenient for patient and clinical staff alike, also hindering recruitment for future large-scale clinical trials. Here, for the first time, we demonstrate rapid, remote replication of UPDRS assessment with clinically useful accuracy (about 7.5 UPDRS points difference from the clinicians' estimates), using only simple, self-administered, and noninvasive speech tests. We characterize speech with signal processing algorithms, extracting clinically useful features of average PD progression. Subsequently, we select the most parsimonious model with a robust feature selection algorithm, and statistically map the selected subset of features to UPDRS using linear and nonlinear regression techniques that include classical least squares and nonparametric classification and regression trees. We verify our findings on the largest database of PD speech in existence (~6000 recordings from 42 PD patients, recruited to a six-month, multicenter trial). These findings support the feasibility of frequent, remote, and accurate UPDRS tracking. This technology could play a key part in telemonitoring frameworks that enable large-scale clinical trials into novel PD treatments.

Subfields: Physiology, Signal Processing

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Citations in the corpus

Hyperparameter optimization with approximate gradient (2016, background): “This dataset is composed of a range of biomedical voice measurements from 42 people with early-stage Parkinson’s disease(Tsanas et al., 2010).”
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization (2019, methodology): “We use 4 popular UCI (Lichman, 2013) datasets for regression: protein structure (Rana, 2013), slice localization (Graf et al., 2011), naval propulsion (Coraddu et al., 2014) and parkinsons telemonitoring (Tsanas et al., 2010).”
Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings (2013, methodology): “[16] used speech data to predict tendency for progression of disease in which they extracted voice features using signal processing algorithms from 6000 samples of 42 PWP and identiﬁed the useful features.”
Domain Generalization by Marginal Transfer Learning (2017, methodology): “The aim is to predict the clinician’s Parkinson’s disease symptom score for each recording on the unified Parkinson’s disease rating scale (UPDRS) (Tsanas et al., 2010).”
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation (2019, methodology): “As a first test, we trained a fully-connected DNN on the Parkinsons Telemonitoring Data Set (Tsanas et al., 2010).”
Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools (2012, methodology): “all the variables are normalized to lie between 0 and 1) to facilitate comparison between measures that possibly span orders of magnitude different ranges of values [22].”
Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease (2012, methodology): “In our own research, we have also presented strong evidence linking speech to average Parkinson’s disease symptom severity [5], [10]–[13].”
Learning accurate and interpretable models based on regularized random forests regression (2014, background): “The Parkinson’s Telemonitoring data set [12] contains biomedical voice measurement from 42 people with early-stage Parkinson’s disease.”